Аналіз сигналів електричної активності м’язів для діагностики захворювань
| dc.contributor.advisor | Іванушкіна, Наталія Георгіївна | |
| dc.contributor.author | Мушта, Семен Андрійович | |
| dc.date.accessioned | 2023-08-29T13:23:09Z | |
| dc.date.available | 2023-08-29T13:23:09Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Об’єктом розгляду є сигнали електричної активності м’язів. Предмет роботи – аналіз сигналів електроміограм, зібраних за допомогою системи моніторингу ЕМГ Synergy N2 (Oxford Instruments Medical, Old Woking, United Kingdom). Метою роботи є розробка методики обробки сигналів ЕМГ з використанням методів цифрової обробки сигналів. У першому розділі розглянуто загальні положення про функціонування опорно-рухового апарату, біофізику мембрани клітини, виникненння і поширення потенціалу дії вздовж мембрани, теорії скорочення м’яза, особливості нервово-м’язових захворювань. У другому розділі описані особливості вимірювання ЕМГ, генезис електроміосигналів та види стимулюючих електродів. У третьому розділі розглядаються основні методи цифрової обробки сигналів. Четвертий рοзділ присвячено розробці методики обробки сигналів ЕМГ з використанням дискретного вейвлет-перетворення. | uk |
| dc.description.abstractother | The subject of consideration is the signals of electrical activity of the muscles. The subject of the work is the analysis of signals of electromyograms, collected by the system of monitoring of EMG Synergy N2 (Oxford Instruments Medical, Old Woking, Great Britain). The purpose of the work is the development of method of EMG signals processing using digital wavelet transform. The first section contains general statements about function of musculoskeletal system, biophysics of cell membrane, initiation and propagation of the action potential along the cell membrane, characteristics of neuromuscular diseases etc. The features of EMG measurement, genesis of electromyosignals and types of stimulating electrodes were described in the second section. In the third section the main methods of digital signal processing were shown. The fourth section is about the development of methods for EMG signals processing using discrete wavelet processing. 1. The goal of the material in the first section is to provide a very brief introduction to skeletal muscle, it`s structure, and its electrophysiological and contractile properties. This text is an introduction to electrophysiology, following a quantitative approach. The first section summarizes much of the bioelectric processings such as muscle contraction, structure of the myofibril, sliding filament theory etc. Electrode considerations, electrode–tissue interface, electrode materials were also shown. A whole muscle can be divided into separate bundles. Each bundle contains many individual fibers. The fiber is the basic (smallest) functional unit (it constitutes a single cell). It is bounded by a plasma membrane and a thin sheet of connective tissue, the endomysium. The bundles are also surrounded by a connective tissue sheet, the perimysium, which delineates specific fascicles. The whole muscle is encased in its connective tissue sheet, namely, the epimysium. Most skeletal muscles begin and end in tendons. Muscle fibers lie parallel to each other, so the force of contraction contributed by each is additive. In this chapter attention was primarily directed to the electromechanical properties of the single muscle fiber. Another important thing that was described is theaction potential. The functions of the nervous system-sensation, integration, and response-depend on the functions of the neurons underlying these pathways. To understand how neurons are able to communicate, it is necessary to describe the role of an excitable membrane in generating these signals. The basis of this process is the action potential. An action potential is a predictable change in membrane potential that occurs due to the open and closing of voltage gated ion channels on the cell membrane. Electrically active cell membranes, the membrane potential, the action potential, propagation of the action potential were shown. The events of the action potential can be related to specific changes in the membrane voltage. At rest, the membrane voltage is near -70 mV. The membrane begins to depolarize when an external stimulus is applied. The membrane voltage begins a rapid rise toward +30 mV. Then the membrane voltage starts to return to a negative value. Repolarization continues past the resting membrane voltage, resulting in hyperpolarization. The membrane voltage returns to the resting value shortly after hyperpolarization. In addition characteristics of neuromuscular diseases were described. Electrical activity diagnosis, as an extension of the neurologic evaluation, employs the same anatomic principles of localization as clinical examination, searching for evidence of motor and sensory compromise. Neurophysiologic studies supplement the history and physical examination, adding precision, detail, and objectivity. These studies delineate a variety of pathologic changes that may otherwise escape detection, particularly in atrophic, deeply situated, or paretic muscles. In this work myopathy and neuropathy signals were used, so neuropathy and myopathy disorders were described mainly. 2. The goal of the material in the second section is to overview features of EMG measurement, genesis of electromyosignals and types of stimulating electrodes. Electromyography (EMG) is a diagnostic procedure to assess the health of muscles and the nerve cells that control them (motor neurons). EMG results can reveal nerve dysfunction, muscle dysfunction or problems with nerve-to-muscle signal transmission. Motor neurons transmit electrical signals that cause muscles to contract. An EMG uses tiny devices called electrodes to translate these signals into graphs, sounds or numerical values that are then interpreted by a specialist. During a needle EMG, a needle electrode inserted directly into a muscle records the electrical activity in that muscle. A nerve conduction study, another part of an EMG, uses electrode stickers applied to the skin (surface electrodes) to measure the speed and strength of signals traveling between two or more points. A key element in functional electrical stimulation (FES) is the initiation of an action potential on a desired nerve, while at the same time refraining from stimulating other nerves nearby. To work toward this goal requires consideration of the effect of electrode(s) size, shape, and location, and the strength and waveform of the stimulating current. Of course, one also needs to know the nerve geometry, its electrical properties, and that of the volume conductor. In addition to depolarizing currents, hyperpolarizing signals must also be considered when the goal is to block unwanted traffic. Analysis of electrodes, electrode - tissue interface, electrode operating characteristics, types of electrodes for specific applications were shown. 3. The goal of the material in the third section is to overview some basic methods of digital signal processing. Fourier and wavelet bases are the main methods which were described and compared in this section. The discrete wavelet transform is widely used in many applications of science and engineering. A signal conveys some information. Most of the naturally occurring signals are continuous in nature. More often than not, they are converted to digital form and processed. In digital signal processing, the information is extracted using digital devices. It is the availability of fast digital devices and numerical algorithms that has made digital signal processing important for many applications of science and engineering. Often, the information in a signal is obscured by the presence of noise. Some type of filtering or processing of signals is usually required. To transmit and store a signal, we would like to compress it as much as possible with the required fidelity. These tasks have to be carried out in an efficient manner. The form of the signal is changed. Processing of a signal is mostly carried out in a transformed representation. The most commonly used representation is in terms of a set of sinusoidal signals. Fourier analysis is the representation of a signal using constant-amplitude sinusoids. It has four versions to suit discrete or continuous and periodic or aperiodic signals. Fourier analysis enables us to do spectral analysis of a signal. The spectral characterization of a signal is very important in many applications. Even for a certain class of signals, the amplitude profile will vary arbitrarily and system design can be based only on the classification in terms of the spectral characterization. Another important advantage is that complex operations become simpler, when signals are represented in terms of their spectra. For example, convolution in the time domain becomes multiplication in the frequency domain. In most cases, it is easier to analyze, interpret, process, compress, and transmit a signal in a transformed representation. The representation of a signal by a set of basis functions, of transient nature, composed of a set of continuous group of frequency components of the signal spectrum is called the wavelet transform. Obviously, the main task in representing a signal in this form is filtering. The wavelet transform is a new representation of a signal. The discrete wavelet transform (DWT) is widely used in signal and image processing applications, such as analysis, compression, and denoising. This transform is inherently suitable in the analysis of nonstationary signals. There are many transforms used in signal processing. Most of the frequently used transforms, including the DWT, are a generalization of the Fourier analysis. 4. The fourth section is about the development of methods for EMG signals processing using discrete wavelet processing. According to the signal's characteristics it was decided to use discrete wavelet transform with the use of the mother Mayer function, which at different time scales is similar to the investigated signals. All EMG were pre-filtered using a general soft trasholding. Also the AFC of the signals with the use of Fourier transform were built, in order to verify that this method of processing is not effective in the application to investigated EMG. During analyses of the AFC, it was almost impossible to identify characteristics for further classification. | uk |
| dc.format.extent | 118 с. | uk |
| dc.identifier.citation | Мушта, C. А. Аналіз сигналів електричної активності м’язів для діагностики захворювань : дипломна робота … бакалавра : 153 Мікро- та наносистемна техніка / Мушта Семен Андрійович. – Київ, 2021. – 118 с. | uk |
| dc.identifier.uri | https://ela.kpi.ua/handle/123456789/59627 | |
| dc.language.iso | uk | uk |
| dc.publisher | КПІ ім. Ігоря Сікорського | uk |
| dc.publisher.place | Київ | uk |
| dc.subject | електрична активність мʼязів | uk |
| dc.subject | аналіз міоелектричних сигналів | uk |
| dc.subject | обробка сигналів ЕМГ | uk |
| dc.title | Аналіз сигналів електричної активності м’язів для діагностики захворювань | uk |
| dc.type | Bachelor Thesis | uk |
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